Name
Abstract | ||
|---|---|---|
In this paper, we investigate the detection of semantic human actions in complex scenes. Unlike conventional action recognition in well-controlled environments, action detection in complex scenes suffers from cluttered backgrounds, heavy crowds, occluded bodies, and spatial-temporal boundary ambiguities caused by imperfect human detection and tracking. Conventional algorithms are likely to fail with such spatial-temporal ambiguities. In this work, the candidate regions of an action are treated as a bag of instances. Then a novel multiple-instance learning framework, named SMILE-SVM (Simulated annealing Multiple Instance LEarning Support Vector Machines), is presented for learning human action detector based on imprecise action locations. SMILE-SVM is extensively evaluated with satisfactory performances on two tasks: (1) human action detection on a public video action database with cluttered backgrounds, and (2) a real world problem of detecting whether the customers in a shopping mall show an intention to purchase the merchandise on shelf (even if they didn't buy it eventually). In addition, the complementary nature of motion and appearance features in action detection are also validated, demonstrating a boosted performance in our experiments. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICCV.2009.5459153 | ICCV |
Keywords | DocType | Volume |
semantic networks,video action database,simulated annealing multiple instance learning support vector machines,human action detection,object detection,smile-svm,occluded bodies,cluttered backgrounds,semantic human actions detection,simulated annealing,spatial-temporal boundary ambiguities,real world problem,support vector machines,feature extraction,labeling,machine learning | Conference | 2009 |
Issue | ISSN | ISBN |
1 | 1550-5499 E-ISBN : 978-1-4244-4419-9 | 978-1-4244-4419-9 |
Citations | PageRank | References |
50 | 1.79 | 24 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuxiao Hu | 1 | 2209 | 103.06 |
| liangliang cao | 2 | 1816 | 90.71 |
| Fengjun Lv | 3 | 2490 | 87.05 |
| Shuicheng Yan | 4 | 1970 | 74.15 |
| yihong gong | 5 | 7300 | 470.57 |
| Thomas S. Huang | 6 | 27815 | 2618.42 |